Skip to main content
SpringerLink
Log in

Metabonomic Phenotyping for the Gut Microbiota and Mammal Interactions

  • Chapter
Infectious Microecology

Part of the book series: Advanced Topics in Science and Technology in China ((ATSTC))

  • 702 Accesses

Abstract

All mammals consist of two distinct but integrated parts including hosts themselves and some symbiotic microorganisms [1–3]. Their symbiosis is established interactively through co-evolution and mutual selections [3–5]. Therefore, mammals are regarded as ‘superorganisms’ and their physiology and health in entirety have to be understood by taking into consideration hosts, symbiotic microbes and their interactions [1–4]. The symbiotic microorganisms are living mostly in the mammals’ gut and also known in different contexts as the gut microbiota, microparasites and microbiomes. It is now known that mammals harbor trillions of symbiotic microbes mainly in their gastrointestinal tract (GIT) with many different microbial species [2–7]. In normal adult human GIT, for instance, there is more than one kilogram of microbes with over ten times more cells than hosts and several thousands of species [2–7]. These symbiotic gut microbiota are co-developed with their hosts’ growth playing essential roles in many aspects of mammalian physiology and thus have profound effects on the hosts’ health [3–7]. For this reason, microbiomes are now considered collectively as an ‘essential organ’ or extended genomes, transcriptomes, proteomes and metabonomes [4, 7, 8] for their mammalian hosts. However, it is nontrivial at the moment to completely define the genomes of these microbiomes as has been done for human and rodent hosts. Neither can their composition, transcriptomes and proteomes be defined in detail, since many species cannot be cultured ex vivo.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Lederberg J. Infectious history. Science, 2000, 288: 287–293.

    Article  CAS  PubMed  Google Scholar 

  2. Qin J J, Li R Q, Raes J, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature, 2010, 464: 59–65.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  3. Xu J, Bjursell M K, Himrod J, et al. A genomic view of the human-bacteroides thetaiotaomicron symbiosis. Science, 2003, 299: 2074–2076.

    Article  CAS  PubMed  Google Scholar 

  4. Nicholson J K, Holmes E, Wilson I D. Gut microorganisms, mammalian metabolism and personalized health care. Nat Rev Microbiol, 2005, 3: 431–438.

    Article  CAS  PubMed  Google Scholar 

  5. Ley R E, Hamady M, Lozupone C, et al. Evolution of mammals and their gut microbes. Science, 2008, 320: 1647–1651.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  6. Backhed F, Ley R E, Sonnenburg J L, et al. Host-bacterial mutualism in the human intestine. Science, 2005, 307: 1915–1920.

    Article  PubMed  CAS  Google Scholar 

  7. Yatsunenko T, Rey F E, Manary M J, et al. Human gut microbiome viewed across age and geography. Nature, 2012, 486: 222–227.

    PubMed Central  CAS  PubMed  Google Scholar 

  8. Nicholson J K, Holmes E, Kinross J, et al. Host-gut microbiota metabolic interactions. Science, 2012, 336: 1262–1267.

    Article  CAS  PubMed  Google Scholar 

  9. Xu J, Chiang H C, Bjursell M K, et al. Message from a human gut symbiont: sensitivity is a prerequisite for sharing. Trends Microbiol, 2004, 12: 21–28.

    Article  PubMed  CAS  Google Scholar 

  10. Hooper L V, Gordon J I. Commensal host-bacterial relationships in the gut. Science, 2001, 292: 1115–1118.

    Article  CAS  PubMed  Google Scholar 

  11. Hooper L V, Littman D R, Macpherson A J. Interactions between the microbiota and the immune system. Science, 2012, 336: 1268–1273.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  12. Macpherson A, Khoo U Y, Forgacs I, et al. Mucosal antibodies in inflammatory bowel disease are directed against intestinal bacteria. Gut, 1996, 38: 365–375.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  13. Peterson D A, McNulty N P, Guruge J L, et al. Iga response to symbiotic bacteria as a mediator of gut homeostasis. Cell Host & Microbe, 2007, 2: 328–339.

    Article  CAS  Google Scholar 

  14. Kau A L, Ahern P P, Griffin N W, et al. Human nutrition, the gut microbiome and the immune system. Nature, 2011, 474: 327–336.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  15. Garrett W S, Gordon J I, Glimcher L H. Homeostasis and inflammation in the intestine. Cell, 2010, 140: 859–870.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  16. Sanz Y, Santacruz A, Gauffin P. Gut microbiota in obesity and metabolic disorders. Proc Nutri Soc, 2010, 69: 434–441.

    Article  CAS  Google Scholar 

  17. Turnbaugh P J, Hamady M, Yatsunenko T, et al. A core gut microbiome in obese and lean twins. Nature, 2009, 457: 480–487.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  18. Ley R E, Backhed F, Turnbaugh P, et al. Obesity alters gut microbial ecology. Proc Natl Acad Sci USA, 2005, 102: 11070–11075.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  19. Ley R E, Tumbaugh P J, Klein S, et al. Microbial ecology-human gut microbes associated with obesity. Nature, 2006, 444: 1022–1023.

    Article  CAS  PubMed  Google Scholar 

  20. Turnbaugh P J, Ley R E, Mahowald M A, et al. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature, 2006, 444: 1027–1031.

    Article  PubMed  Google Scholar 

  21. Marchesi J R, Holmes E, Khan F, et al. Rapid and non-invasive metabonomic characterisation of inflammatory bowel disease. J Proteome Res, 2007, 6: 546–552.

    Article  CAS  PubMed  Google Scholar 

  22. Zhang X Y, Wang Y L, Hao F H, et al. Human serum metabonomic analysis reveals progression axes for glucose intolerance and insulin resistance statuses. J Proteome Res, 2009, 8: 5188–5195.

    Article  CAS  PubMed  Google Scholar 

  23. Backhed F, Ding H, Wang T, et al. The gut microbiota as an environmental factor that regulates fat storage. Proc Natl Acad Sci USA, 2004, 101: 15718–15723.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  24. Tian Y, Zhang L M, Wang Y L, et al. Age-related topographical metabolic signatures for the rat gastrointestinal contents. J Proteome Res, 2012, 11: 1397–1411.

    Article  CAS  PubMed  Google Scholar 

  25. Martin F P J, Dumas M E, Wang Y L, et al. A top-down systems biology view of microbiome- mammalian metabolic interactions in a mouse model. Mol Systems Biol, 2007, 3: article 112,

    Article  CAS  Google Scholar 

  26. Swann J R, Want E J, Geier F M, et al. Systemic gut microbial modulation of bile acid metabolism in host tissue compartments. Proc Natl Acad Sci USA, 2011, 108: 4523–4530.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  27. Li J V, Ashrafian H, Bueter M, et al. Metabolic surgery profoundly influences gut microbial-host metabolic cross-talk. Gut, 2011, 60: 1214–1223.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  28. Ridlon J M, Kang D J, Hylemon P B. Bile salt biotransformations by human intestinal bacteria. J Lipid Res, 2006, 47: 241–259.

    Article  CAS  PubMed  Google Scholar 

  29. Jones B V, Begley M, Hill C, et al. Functional and comparative metagenomic analysis of bile salt hydrolase activity in the human gut microbiome. Proc Natl Acad Sci USA, 2008, 105: 13580–13585.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  30. O’Keefe S J D, Ou J H, Aufreiter S, et al. Products of the colonic microbiota mediate the effects of diet on colon cancer risk. J Nutr, 2009, 139: 2044–2048.

    Article  PubMed  CAS  Google Scholar 

  31. Hope M E, Hold G L, Kain R, et al. Sporadic colorectal cancer — Role of the commensal microbiota. FEMS Microbiol Lett, 2005, 244: 1–7.

    Article  CAS  PubMed  Google Scholar 

  32. Stepankova R, Tonar Z, Bartova J, et al. Absence of microbiota (germ-free conditions) accelerates the atherosclerosis in apoe-deficient mice fed standard low cholesterol diet. J Atheroscl Thromb, 2010, 17: 796–804.

    Article  CAS  Google Scholar 

  33. Rhee S H, Pothoulakis C, Mayer E A. Principles and clinical implications of the brain-gut-enteric microbiota axis. Nat Rev Gastroenterol Hepatol, 2009, 6: 306–314.

    Article  CAS  PubMed  Google Scholar 

  34. Parracho HMRT, Bingham M O, Gibson G R, et al. Differences between the gut microflora of children with autistic spectrum disorders and that of healthy children. J Med Microbiol, 2005, 54: 987–991.

    Article  PubMed  Google Scholar 

  35. Xu W X, Wu J F, An Y P, et al. Streptozotocin-induced dynamic metabonomic changes in rat biofluids. J Proteome Res, 2012, 11: 3423–3435.

    Article  CAS  PubMed  Google Scholar 

  36. Dumas M E, Barton R H, Toye A, et al. Metabolic profiling reveals a contribution of gut microbiota to fatty liver phenotype in insulin-resistant mice. Proc Natl Acad Sci USA, 2006, 103: 12511–12516.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  37. Clayton T A, Lindon J C, Cloarec O, et al. Pharmaco-metabonomic phenotyping and personalized drug treatment. Nature, 2006, 440: 1073–1077.

    Article  CAS  PubMed  Google Scholar 

  38. Clayton T A, Baker D, Lindon J C, et al. Pharmacometabonomic identification of a significant host-microbiome metabolic interaction affecting human drug metabolism. Proc Natl Acad Sci USA, 2009, 106: 14728–14733.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  39. Wilson I D, Nicholson J K. The role of gut microbiota in drug response. Curr Pharmaceut Design, 2009, 15: 1519–1523.

    Article  CAS  Google Scholar 

  40. Li M, Wang B H, Zhang M H, et al. Symbiotic gut microbes modulate human metabolic phenotypes. Proc Natl Acad Sci USA, 2008, 105: 2117–2122.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  41. Hooper L V, Wong M H, Thelin A, et al. Molecular analysis of commensal host-microbial relations hips in the intestine. Science, 2001, 291: 881–884.

    Article  CAS  PubMed  Google Scholar 

  42. Zheng X J, Xie G X, Zhao A H, et al. The footprints of gut microbial-mammalian co-metabolism. J Proteome Res, 2011, 10: 5512–5522.

    Article  CAS  PubMed  Google Scholar 

  43. Nicholson J K, Wilson I D. Understanding ‘global’ systems biology: Metabonomics and the continuum of metabolism. Nat Rev Drug Discov, 2003, 2: 668–676.

    Article  CAS  PubMed  Google Scholar 

  44. Wang Y L, Holmes E, Nicholson J K, et al. Metabonomic investigations in mice infected with schistosoma mansoni: An approach for biomarker identification. Proc Natl Acad Sci USA, 2004, 101: 12676–12681.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  45. Wang Y L, Utzinger J, Saric J, et al. Global metabolic responses of mice to trypanosoma brucei brucei infection. Proc Natl Acad Sci USA, 2008, 105: 6127–6132.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  46. Zhang L M, Ye Y F, An Y P, et al. Systems responses of rats to aflatoxin b1 exposure revealed with metabonomic changes in multiple biological matrices. J Proteome Res, 2011, 10: 614–623.

    Article  CAS  PubMed  Google Scholar 

  47. Martin F P J, Wang Y L, Sprenger N, et al. Top-down systems biology integration of conditional prebiotic modulated transgenomic interactions in a humanized microbiome mouse model. Mol Systems Biol, 2008, 4:article 205.

    Google Scholar 

  48. Wang Z N, Klipfell E, Bennett B J, et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature, 2011, 472: 57–82.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  49. Fukuda S, Toh H, Hase K, et al. Bifidobacteria can protect from enteropathogenic infection through production of acetate. Nature, 2011, 469: 543–791.

    Article  CAS  PubMed  Google Scholar 

  50. Scheppach W. Effects of short-chain fatty-acids on gut morphology and function. Gut, 1994, 35: S35-S38.

    Article  Google Scholar 

  51. Wong J M W, de Souza R, Kendall C W C, et al. Colonic health: Fermentation and short chain fatty acids. J Clin Gastroenterol, 2006, 40: 235–243.

    Article  CAS  PubMed  Google Scholar 

  52. Nicholson J K, Connelly J, Lindon J C, et al. Metabonomics: A platform for studying drug toxicity and gene function. Nat Rev Drug Discov, 2002, 1: 153–161.

    Article  CAS  PubMed  Google Scholar 

  53. Wijeyesekera A, Selman C, Barton R H, et al. Metabotyping of long-lived mice using h-1 nmr spectroscopy. J Proteome Res, 2012, 11: 2224–2235.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  54. Kinross J M, Holmes E, Darzi A W, et al. Metabolic phenotyping for monitoring surgical patients. Lancet, 2011, 377: 1817–1819.

    Article  PubMed  Google Scholar 

  55. Holmes E, Loo R L, Stamler J, et al. Human metabolic phenotype diversity and its association with diet and blood pressure. Nature, 2008, 453: 396–400.

    Article  CAS  PubMed  Google Scholar 

  56. Nicholson J K, Lindon J C. Systems biology-metabonomics. Nature, 2008, 455: 1054–1056.

    Article  CAS  PubMed  Google Scholar 

  57. Holmes E, Wilson I D, Nicholson J K. Metabolic phenotyping in health and disease. Cell, 2008, 134: 714–717.

    Article  CAS  PubMed  Google Scholar 

  58. Martin FPJ, Collino S, Rezzi S. 1H NMR-based metabonomic applications to decipher gut microbial metabolic influence on mammalian health. Magn Reson Chem, 2011, 49: S47-S54.

    Article  CAS  Google Scholar 

  59. Martin F P J, Sprenger N, Montoliu I, et al. Dietary modulation of gut functional ecology studied by fecal metabonomics. J Proteome Res, 2010, 9: 5284–5295.

    Article  CAS  PubMed  Google Scholar 

  60. Martin F P J, Wang Y, Yap I K S, et al. Topographical variation in murine intestinal metabolic profiles in relation to microbiome speciation and functional ecological activity. J Proteome Res, 2009, 8: 3464–3474.

    Article  CAS  PubMed  Google Scholar 

  61. Nicholson J K, Holmes E, Lindon J C, et al. The challenges of modeling mammalian biocomplexity. Nat Biotechnol, 2004, 22: 1268–1274.

    Article  CAS  PubMed  Google Scholar 

  62. Tang H R,Wang Y L. Metabonomics: A revolution in progress. Prog Biochem Biophys, 2006, 33: 401–417.

    CAS  Google Scholar 

  63. Tian J, Shi C Y, Gao P, et al. Phenotype differentiation of three e-coli strains by gc-fid and gc-ms based metabolomics. J Chromat Anal Technol Biomed Life Sci, 2008, 871: 220–226.

    Article  CAS  Google Scholar 

  64. Wilson I D, Plumb R, Granger J, et al. HPLC-MS-based methods for the study of metabonomics. J Chromat Anal Technol Biomed Life Sci, 2005, 817: 67–76.

    Article  CAS  Google Scholar 

  65. Lenz E M, Wilson I D. Analytical strategies in metabonomics. J Proteome Res, 2007, 6: 443–458.

    Article  CAS  PubMed  Google Scholar 

  66. Humpfer E, Spraul M, Nicholls A W, et al. Direct observation of resolved intracellular and extracellular water signals in intact human red blood cells using 1H MAS NMR spectroscopy. Magn Reson Med, 1997, 38: 334–336.

    Article  CAS  PubMed  Google Scholar 

  67. Cheng L L, Ma M J, Becerra L, et al. Quantitative neuropathology by high resolution magic angle spinning proton magnetic resonance spectroscopy. Proc Natl Acad Sci USA, 1997, 94: 6408–6413.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  68. Cheng L L, Chang I W, Louis D N, et al. Correlation of high-resolution magic angle spinning proton magnetic resonance spectroscopy with histopathology of intact human brain tumor specimens. Cancer Res, 1998, 58: 1825–1832.

    CAS  PubMed  Google Scholar 

  69. Ding L N, Hao F H, Shi Z M, et al. Systems biological responses to chronic perfluorododecanoic acid exposure by integrated metabonomic and transcriptomic studies. J Proteome Res, 2009, 8: 2882–2891.

    Article  CAS  PubMed  Google Scholar 

  70. Yang Y X, Li C L, Nie X, et al. Metabonomic studies of human hepatocellular carcinoma using high-resolution magic-angle spinning 1H NMR spectroscopy in conjunction with multivariate data analysis. J Proteome Res, 2007, 6: 2605–2614.

    Article  CAS  PubMed  Google Scholar 

  71. Beckmann M, Parker D, Enot D P, et al. High-throughput, nontargeted metabolite fingerprinting using nominal mass flow injection electrospray mass spectrometry. Nature Protocols, 2008, 3: 486–504.

    Article  CAS  PubMed  Google Scholar 

  72. Fonville J M, Carter C, Cloarec O, et al. Robust data processing and normalization strategy for maldi mass spectrometric imaging. Anal Chem, 2012, 84: 1310–1319.

    Article  CAS  PubMed  Google Scholar 

  73. Nemes P, Woods A S, Vertes A. Simultaneous imaging of small metabolites and lipids in rat brain tissues at atmospheric pressure by laser ablation electrospray ionization mass spectrometry. Anal Chem, 2010, 82: 982–988.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  74. Koizumi S, Yamamoto S, Hayasaka T, et al. Imaging mass spectrometry revealed the production of lyso-phosphatidylcholine in the injured ischemic rat brain. Neuroscience, 2010, 168: 219–225

    Article  CAS  PubMed  Google Scholar 

  75. Cooks R G, Ouyang Z, Takats Z, et al. Ambient mass spectrometry. Science, 2006, 311: 1566–1570.

    Article  CAS  PubMed  Google Scholar 

  76. Dai H, Xiao C N, Liu H B, et al. Combined NMR and LC-MS analysis reveals the metabonomic changes in salvia miltiorrhiza bunge induced by water depletion. J Proteome Res, 2010, 9: 1460–1475.

    Article  CAS  PubMed  Google Scholar 

  77. Dai H, Xiao C N, Liu H B, et al. Combined NMR and LC-DAD-MS analysis reveals comprehensive metabonomic variations for three phenotypic cultivars of salvia miltiorrhiza bunge. J Proteome Res, 2010, 9: 1565–1578.

    Article  CAS  PubMed  Google Scholar 

  78. Holmes E, Loo R L, Cloarec O, et al. Detection of urinary drug metabolite (xenometabolome) signatures in molecular epidemiology studies via statistical total correlation (NMR) spectroscopy. Anal Chem, 2007, 79: 2629–2640.

    Article  CAS  PubMed  Google Scholar 

  79. Cloarec O, Campbell A, Tseng L H, et al. Virtual chromatographic resolution enhancement in cryoflow LC-NMR experiments via statistical total correlation spectroscopy. Anal Chem, 2007, 79: 3304–3311.

    Article  CAS  PubMed  Google Scholar 

  80. Smith L M, Maher A D, Cloarec O ,et al. statistical correlation and projection methods for improved information recovery from diffusion-edited NMR spectra of biological samples. Anal Chem, 2007, 79: 5682–5689.

    Article  CAS  PubMed  Google Scholar 

  81. Wang Y L, Cloarec O, Tang H R, et al. Magic angle spinning NMR and 1H-31P heteronuclear statistical total correlation spectroscopy of intact human gut biopsies. Anal Chem, 2008, 80: 1058–1066.

    Article  CAS  PubMed  Google Scholar 

  82. Maher A D, Fonville J M, Coen M, et al. Statistical total correlation spectroscopy scaling for enhancement of metabolic information recovery in biological NMR spectra. Anal Chem, 2012, 84: 1083–1091.

    Article  CAS  PubMed  Google Scholar 

  83. Cloarec O, Dumas M E, Craig A, et al. Statistical total correlation spectroscopy: An exploratory approach for latent biomarker identification from metabolic 1H NMR data sets. Anal Chem, 2005, 77: 1282–1289.

    Article  CAS  PubMed  Google Scholar 

  84. Crockford D J, Lindon J C, Cloarec O, et al. Statistical search space reduction and two-dimensional data display approaches for UPLC-MS in biomarker discovery and pathway analysis. Anal Chem, 2006, 78: 4398–4408.

    Article  CAS  PubMed  Google Scholar 

  85. Lommen A, Godejohann M, Venema D P, et al. Application of directly coupled HPLC-NMR-MS to the identification and confirmation of quercetin glycosides and phloretin glycosides in apple peel. Anal Chem, 2000, 72: 1793–1797.

    Article  CAS  PubMed  Google Scholar 

  86. Duarte I F, Godejohann M, Braumann U, et al. Application of NMR spectroscopy and LC-NMR/MS to the identification of carbohydrates in beer. J Agri Food Chem, 2003, 51: 4847–4852.

    Article  CAS  Google Scholar 

  87. Corcoran O, Spraul M. LC-NMR-MS in drug discovery. DDT, 2003, 8: 624–631.

    Article  CAS  PubMed  Google Scholar 

  88. Spraul M, Freund A S, Nast R E, et al. Advancing NMR sensitivity for LC-NMR-MS using a cryoflow probe: Application to the analysis of acetaminophen metabolites in urine. Anal Chem, 2003, 75: 1536–1541.

    Article  CAS  PubMed  Google Scholar 

  89. Holmes E, Tang H R, Wang Y L, et al. The assessment of plant metabolite profiles by NMR-based methodologies. Plant Med, 2006, 72: 771–785.

    Article  CAS  Google Scholar 

  90. Tang H R, Xiao C N, Wang Y L. Important roles of the hyphenated HPLC-DAD-SPE-MS/NMR technique in metabonomics. Magn Reson Chem, 2009, 47: S157–S162.

    Article  CAS  Google Scholar 

  91. Duarte I F, Legido-Quigley C, Parker D A, et al. Identification of metabolites in human hepatic bile using 800 MHz 1H NMR spectroscopy, HPLC-NMR/MS and UPLC-MS. Mol Biosys, 2009, 5: 180–190.

    Article  CAS  Google Scholar 

  92. Holmes E, Bonner F W, Sweatman B C, et al. Nuclear-magnetic-resonance spectroscopy and pattern-recognition analysis of the biochemical processes associated with the progression of and recovery from nephrotoxic lesions in the rat induced by mercury(II) chloride and 2-bromoethanamine. Mol Pharmacol, 1992, 42: 922–930.

    CAS  PubMed  Google Scholar 

  93. Ghauri F Y K, Nicholson J K, Sweatman B C, et al. NMR spectroscopy of human postmortem cerebrospinal-fluid-distinction of alzheimers-disease from control using pattern- recognition and statistics. NMR Biomed, 1993, 6: 163–167.

    Article  CAS  PubMed  Google Scholar 

  94. Gartland K P R, Beddell C R, Lindon J C, et al. A pattern-recognition approach to the comparison of PMR and clinical chemical-data for classification of nephrotoxicity. J Pharm Biomed Anal, 1990, 8: 963–968.

    Article  CAS  PubMed  Google Scholar 

  95. Lindon J C, Nicholson J K, Holmes E, et al. Summary recommendations for standardization and reporting of metabolic analyses. Nat Biotechnol, 2005, 23: 833–838.

    Article  CAS  PubMed  Google Scholar 

  96. Eriksson L, Trygg J, Wold S. CV-ANOVA for significance testing of PLS and OPLS (r) models. J Chemometr, 2008, 22: 594–600.

    Article  CAS  Google Scholar 

  97. Trygg J, Wold S. Orthogonal projections to latent structures (O-PLS). J Chemometr, 2002, 16: 119–128.

    Article  CAS  Google Scholar 

  98. Wang Y L, Tang H R, Holmes E, et al. Biochemical characterization of rat intestine development using high-resolution magic-angle-spinning 1H NMR spectroscopy and multivariate data analysis. J Proteome Res, 2005, 4: 1324–1329.

    Article  CAS  PubMed  Google Scholar 

  99. Wang Y L, Holmes E, Comelli E M, et al. Topographical variation in metabolic signatures of human gastrointestinal biopsies revealed by high-resolution magic-angle spinning 1H NMR spectroscopy. J Proteome Res, 2007, 6: 3944–3951.

    Article  CAS  PubMed  Google Scholar 

  100. Hopkins M J, Sharp R, Macfarlane G T. Age and disease related changes in intestinal bacterial popular-ions assessed by cell culture, 16S rRNA abundance, and community cellular fatty acid profiles. Gut, 2001, 48: 198–205.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  101. Wu J F, An Y P, Yao J W, et al. An optimised sample preparation method for NMR-based faecal metabonomic analysis. Analyst, 2010, 135: 1023–1030.

    Article  CAS  PubMed  Google Scholar 

  102. Saric J, Wang Y, Li J, et al. Species variation in the fecal metabolome gives insight into differential gastrointestinal function. J Proteome Res, 2008, 7: 352–360.

    Article  CAS  PubMed  Google Scholar 

  103. Le Gall G, Noor S O, Ridgway K, et al. Metabolomics of fecal extracts detects altered metabolic activity of gut microbiota in ulcerative colitis and irritable bowel syndrome. J Proteome Res. 2011. 10: 4208–4218.

    Article  CAS  PubMed  Google Scholar 

  104. Cao H C, Huang H J, Xu W, et al. Fecal metabolome profiling of liver cirrhosis and hepatocellular carcinoma patients by ultra performance liquid chromatography-mass spectrometry. Anal Chim Acta, 2011, 691: 68–75.

    Article  CAS  PubMed  Google Scholar 

  105. Naruse S, Ishiguro H, Ko S B H, et al. Fecal pancreatic elastase: A reproducible marker for severe exocrine pancreatic insufficiency. J Gastroenterol, 2006, 41: 901–908.

    Article  CAS  PubMed  Google Scholar 

  106. Hu S, Dong T S, Dalal S R, et al. The microbe-derived short chain fatty acid butyrate targets miRNA-dependent p21 gene expression in human colon cancer. Plos One, 2011, 6: e16221.

    Google Scholar 

  107. Jacobs D MDeltimple N, van Velzen E, et al., 1H NMR metabolite profiling of feces as a tool to assess the impact of nutrition on the human microbiome. NMR Biomed, 2008, 21: 615–626.

    Article  CAS  PubMed  Google Scholar 

  108. Holmes E, Kinross J, Gibson G R, et al. Therapeutic modulation of microbiota-host metabolic interactions. Sci Transl Med, 2012, 4: 137–142.

    Article  CAS  Google Scholar 

  109. Wang X N, Wang X Y, Xie G X, et al. Urinary metabolite variation is associated with pathological progression of the post-hepatitis B cirrhosis patients. J Proteome Res, 2012, 11: 3838–3847.

    Article  CAS  PubMed  Google Scholar 

  110. Cheng Y, Xie G X, Chen T L, et al. Distinct urinary metabolic profile of human colorectal cancer. J Proteome Res, 2012, 11: 1354–1363.

    Article  CAS  PubMed  Google Scholar 

  111. Wang Y L, Tang H R, Nicholson J K, et al. A metabonomic strategy for the detection of the metabolic effects of chamomile (matricaria recutita L.) ingestion. J Agri Food Chem, 2005, 53: 191–196.

    Article  CAS  Google Scholar 

  112. Wu J F, Holmes E, Xue J, et al. Metabolic alterations in the hamster co-infected with schistosoma japonicum and necator americanus. Int J Parasitol, 2010, 40: 695–703.

    Google Scholar 

  113. Wu J F, Xu W X, Ming Z P, et al. Metabolic changes reveal the development of schistosomiasis in mice. PLOS Negl Trop Dis, 2010, 4: e807.

    Google Scholar 

  114. Dunn WB, Broadhurst D, Begley P, et al. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nature Protocols, 2011, 6: 1060–1083.

    Article  CAS  PubMed  Google Scholar 

  115. Chan E C Y, Pasikanti K K, Nicholson J K. Global urinary metabolic profiling procedures using gas chromatography-mass spectrometry. Nature Protocols, 2011, 6: 1483–1499.

    Article  CAS  PubMed  Google Scholar 

  116. Beckonert O, Coen M, Keun H C, et al. High-resolution magic-angle-spinning NMR spectroscopy for metabolic profiling of intact tissues. Nature Protocols, 2010, 5: 1019–1032.

    Article  CAS  PubMed  Google Scholar 

  117. Beckonert O, Keun H C, Ebbels T M D, et al. Metabolic profiling, metabolomic and metabonomic procedures for nmr spectroscopy of urine, plasma, serum and tissue extracts. Nature Protocols, 2007, 2: 2692–2703.

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Center for Biospectroscopy and Metabonomics, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan, 430071, China

    Huiru Tang & Yulan Wang

Authors
  1. Huiru Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yulan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. The State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, China

    Lanjuan Li

  2. Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Hangzhou, 310003, China

    Lanjuan Li

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Zhejiang University Press, Hangzhou and Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Tang, H., Wang, Y. (2014). Metabonomic Phenotyping for the Gut Microbiota and Mammal Interactions. In: Li, L. (eds) Infectious Microecology. Advanced Topics in Science and Technology in China. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43883-1_8

Download citation

Publish with us

Policies and ethics

Search

Navigation